1. Report WG1 Software-Intensive Systems and New Computing Paradigms Cannes November 12-14, 2008 WG Leader: Martin Wirsing WG Depu ty Leaders: Jean-Pierre Banatre, Matthias Hölzl

2. 2 Characterisation

3. Physical Ensembles Societal Ensembles ENSEMBLE ENGINEERING Foundations, Modeling and Analysis Languages, Compilers, Platforms Languages, Compilers, Platforms Methods, Tools, Processes Methods, Tools, Processes Global reasoning, predictability, high confidence for SW-intensive automation, control systems, and material programming Local and regionals reasoning about privacy, confidentiality, trustworthiness Hybrid stochastic, continuous and discrete models and reasoning Testbeds Large number of adaptive machines and people interacting non-compositionally cooperative & competitive ecosystem dynamics vs. equilibria Large number of nodes in real- space sensing and effecting environment in real-time, limited communication & space, changing topology and geometry

4. 4 Reference Examples for Ensembles Physical Ensembles Synthetic biology Intelligent nanostructures Bioengineered artificial cells Next-generation robotics Swarms of satellites Flying unmanned air vehicles (UAV swarm) Programmable pervasive transport systems Societal Ensembles Smart cities Ambient assistance Global virtual enterprises

5. 5 Ensemble Engineering Objectives –To enable the design and development of dependable ensembles Novel programming language designs Formal Methods for reasoning about ensembles –Specification of global properties –Techniques for synthesis of the behavior of individual actors to derive global behaviors –Validation and verification –Methods for controlling and adapting ensembles whose subsystems and components are dynamically changing Focus –"dealing with whole/part“ –"handling uncertainty"

6. 6 Focus: „dealing with whole/part„ high-level global specification to local behaviors (manually or automatically) analyze local behavior to discover global properties human participants in ensamble can only be influenced, not controlled (system coordinates human action, humans influence system) self-*(aware, optimization) to achieve detection fo failure, modularity, compartmentalization (stop error propogation) analysis of impact of a change on system dealing with different scales of time/space. Change in system purpose is on different time scale than behavior

7. 7 Focus: "handling uncertainty" Soft specification, try to get system to approximate/achieve specification to degree possble (best effort to meet soft spec) Compile (spec, reflective view of "world") ==> actions –relative to changing world, goals, failures, constraints, etc –"hill-climbining behaivor" -- finding local maximum Create languages for describing soft specification Tools to analyze and check specification Adaptation through reflection, metaprogramming, planing, or program synthesis

8. 8 Approaches 1.Investigate new mathematical models and techniques to facilitate reasoning about the engineering of ensembles of software components. How do we define and establish “fitness for purpose”? New formalisms for decentralized interaction paradigms. Specification and validation of probabilistic properties which specify quantitative behavior in space and time. How do we model and reason about adaptation, stability and equilibrium? 2.Identification and formalization of “design patterns” which achieve desirable global behaviors (such as avoiding chaotic behavior). Examples of such patterns include co-fields, stigmergy, interactions protocols. 3.Application of progress in complex systems research to software artifacts enabling software to respond to dynamic changes in other components and systems in its execution environment.

9. 9 Outcomes Languages and models for creating scalable, robust, flexible and efficient ensembles Methods and tools for declarative global specification, refinement and compilation into local rules running on ensemble elements Theoretical foundations for physical ensembles drawing on thermodynamics, control, automata and logic

10. 10 Physical Ensembles: Focus Engineering: Support creation of ensembles from concise global aggregate specification automatically turned into local rules Science: Understanding global behaviours inferred from local rules Design and realization: Develop architectural design methods for hierarchical ensemble engineering Spatial Models: Real-space and real-time modelling, scheduling and verification of physical ensembles Assurance: Robustness, certifiability and predictable quality of service of physical ensembles Testbeds: develop applications and platforms for exploring and experimenting with ensembles

11. Ensemble flagship successes: e.g. programmable matter in interdisciplinary projects Ensemble user languages, tools and platforms Ensemble models, languages, foundations Ensembles: collaborative engagements Roadmap: Ensemble Engineering Best practices SW-intensive, sensor networks, modular robots and intelligent nanostructures Improvements in SW-intensive systems You are here 2030 2005